Vehicle, driving assistance device and method

ABSTRACT

A driving assistance device for a vehicle includes an acquisition module configured to acquire information comprising at least one of vehicle state information and environment information surrounding the vehicle; a decision module configured to determine a driving behavior for the vehicle in an autonomous driving mode based on the acquired information; a pre-processing module configured to process the acquired information to identify a respective information category among a plurality of predefined information categories the acquired information belongs to; and a determining module configured to retrieve one or more traffic rules related to the information category the acquired information belongs to from a traffic rule database pre-stored in the driving assistance device; determine whether the driving behavior violates any of the retrieved traffic rules; and determine risks of said driving behavior if it is determined the driving behavior violates at least one of the retrieved traffic rules, said risks comprising one or more of the legal penalty, property damages and personal injury caused by the driving behavior.

CROSS-REFERENCE TO RELATED APPLICATION

The present disclosure claims the benefit of priority of co-pendingChinese Patent Application No. CN 202110270446.6, filed on Mar. 12,2021, and entitled “Driving Assistance Device, System and Method forVehicle,” the contents of which are incorporated in full by referenceherein.

TECHNICAL FIELD

The disclosure relates to the technical field of vehicle drivingassistance, in particular to a driving assistance device and method anda vehicle including the driving assistance device.

BACKGROUND

The driving assistance technology is a research hotspot and an importantresearch direction of the automobile industry. In the prior art, therehave been studies on various aspects of a driving assistance system. Theautonomous driving (self-driving) behaviors of the vehicle shouldconform to the traffic rules (the traffic laws), especially when avehicle equipped with a driving assistance system drives in anautonomous driving mode.

In a prior art solution, the driving assistance system detects whether aself-driving vehicle violates any of the traffic rules, and sends thedetected result that the self-driving vehicle violates one or moretraffic rules to a back-end server or traffic control department. Inanother prior art solution, driving assistance system compares detecteddriving behaviors of a self-driving vehicle with standard drivingbehaviors defined by the traffic rules, and intervenes the self-drivingof the vehicle if the detected driving behaviors deviate from thestandard driving behaviors.

It is seen the prior art solutions mainly focus on judging whether adriving behavior of a self-driving vehicle violates any traffic rule.However, the prior art solutions cannot solve the problem of how tominimize the risk of the autonomous driving behavior if a violation oftraffic rules cannot be avoided.

SUMMARY

According to one aspect of the disclosure, a driving assistance devicefor a vehicle is provided. The driving assistance device includes: anacquisition module configured to acquire information comprising at leastone of vehicle state information and environment information surroundingthe vehicle; a decision module configured to determine a drivingbehavior for the vehicle in an autonomous driving mode based on theacquired information; a pre-processing module configured to process theacquired information to identify a respective information category amonga plurality of pre-defined information categories the acquiredinformation belongs to; and a determining module configured to retrieveone or more traffic rules related to the information category theacquired information belongs to from a traffic rule database pre-storedin the driving assistance device; determine whether the driving behaviorviolates any of the retrieved traffic rules; and determine risks of saiddriving behavior if it is determined the driving behavior violates atleast one of the retrieved traffic rules, said risks comprising one ormore of the legal penalty, property damages and personal injury causedby the driving behavior.

According to another aspect of the disclosure, a vehicle. The vehicleincludes: a communication interface configured to receive information onthe vehicle's states, environmental conditions and traffic rules from anexternal device; an on-board sensing unit for capture information on thevehicle's surroundings and states; and a driving assistance device incommunication with the communication interface and the in-vehiclesensing unit, the driving assistance device including: an acquisitionmodule configured to acquire information from the communicationinterface and the in-vehicle sensing unit; a decision module configuredto determine a driving behavior for the vehicle in an autonomous drivingmode based on the acquired information; a pre-processing moduleconfigured to process the acquired information to identify a respectiveinformation category among a plurality of pre-defined informationcategories the acquired information belongs to; and a determining moduleconfigured to retrieve one or more traffic rules related to theinformation category the acquired information belongs to from a trafficrule database pre-stored in the driving assistance device; determinewhether the driving behavior violates any of the retrieved trafficrules; and determine risks of said driving behavior if it is determinedthe driving behavior violates at least one of the retrieved trafficrules, said risks comprising one or more of the legal penalty, propertydamages and personal injury caused by the driving behavior.

According to yet another aspect of the disclosure, a driving assistancemethod for a vehicle is provide. The driving assistance method includesthe steps of: acquiring information including at least one of vehiclestate information and environment information surrounding the vehicle;determining a driving behavior for the vehicle in an autonomous drivingmode based on the acquired information; processing the acquiredinformation to obtain a respective information category among aplurality of pre-defined information categories the acquired informationbelongs to; retrieving one or more traffic rules related to theinformation category the acquired information belongs to from a trafficrule database pre-stored in the driving assistance device; determiningwhether the driving behavior violates any of the retrieved trafficrules; and determining risks of the driving behavior if it is determinedthe driving behavior violates at least one of the retrieved trafficrules, said risks comprising one or more of the legal penalty, propertydamages and personal injury caused by the driving behavior.

According to yet another aspect of the disclosure, a non-transitorycomputer-readable medium with instructions stored therein which, whenexecuted, cause one or more processors to carry out the steps including:acquiring information including at least one of vehicle stateinformation and environment information surrounding the vehicle;determining a driving behavior for the vehicle in an autonomous drivingmode based on the acquired information; processing the acquiredinformation to obtain a respective information category among aplurality of pre-defined information categories the acquired informationbelongs to; retrieving one or more traffic rules related to theinformation category the acquired information belongs to from a trafficrule database pre-stored in the driving assistance device; determiningwhether the driving behavior violates any of the retrieved trafficrules; and determining risks of the driving behavior if it is determinedthe driving behavior violates at least one of the retrieved trafficrules, said risks comprising one or more of the legal penalty, propertydamages and personal injury caused by the driving behavior.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosed aspects will hereinafter be described in connection withthe appended drawings that are provided to illustrate and not to limitthe scope of the disclosure.

FIG. 1A schematically illustrates an exemplary traffic environment inwhich an embodiment of the disclosure can be implemented.

FIG. 1B schematically illustrates another exemplary traffic environmentin which an embodiment of the disclosure can be implemented.

FIG. 2 shows a vehicle according to an embodiment of the disclosure.

FIG. 3 shows a driving assistance device according to an embodiment ofthe disclosure.

FIG. 4 illustrates a process of determining whether to enable anautonomous driving mode according to an embodiment of the presentdisclosure.

FIG. 5A schematically shows a working principle of the drivingassistance device illustrated in FIG. 3.

FIG. 5B shows a driving assistance process according to an embodiment ofthe disclosure.

FIG. 6 is a flowchart of a driving assistance method according to anembodiment of the disclosure.

DETAILED DESCRIPTION

FIG. 1A schematically illustrates an exemplary traffic environment inwhich an embodiment of the disclosure can be implemented. Referring toFIG. 1A, a vehicle V is driving in an autonomous driving mode in thedriving lane L_v, and suddenly a pedestrian appears in front of thevehicle V. At this time, the vehicle V has three options, namely, (1)turning left which will result in a double solid line violation; (2)braking to avoid hitting the pedestrian which will cause a collisionwith the vehicle behind (i.e., the vehicle V1); (3) turning right whichwill result in encroaching onto a bus Lane L_BUS.

FIG. 1B schematically illustrates another exemplary traffic environmentin which an embodiment of the present disclosure can be implemented.Referring to FIG. 1B, the vehicle V in an autonomous driving mode isdriving in the driving lane L_v, and suddenly a pedestrian appears infront of the vehicle V. At this time, vehicle V has three options,namely, (1) turning left which will result in entering a reverse lane onthe left which may result in a collision with an coming truck T; (2)turning right which will result in entering a non-motorized lane L_BIKEon the right which may result in a collision with cyclists on thenon-motorized lane; (3) emergency braking which will result in collidingwith the pedestrian.

When a vehicle is in an autonomous driving mode and is travelling in atraffic environment such as FIG. 1A or FIG. 1B, by employing thetechnical solution according to an example of the present disclosure, anoptimized self-driving strategy can be provided for the vehicle by meansof the technical solution according to an embodiment of the disclosure.Specifically, the technical solution of the present disclosure providessuch a driving assistance strategy that an optimized decision on adriving behavior can be made in an autonomous driving mode based onrisks on legal penalty, property damages and personal injury, inparticular when a violation of traffic rules cannot be avoided. Incontrast, solutions in the prior art only detect vehicle behaviors andjudge whether the vehicle behaviors violate any traffic rule withoutproviding a strategy to reduce the risks and resulting damages.

In an embodiment of the disclosure, a closed-loop control is performedand concluded when a stable decision on a driving behavior is output.According to an embodiment of the disclosure, it improves the vehicle'sability to recognize traffic rules, and it also contributes to make anoptimal decision on a driving behavior. In the case that a violation ofat least one traffic rule cannot be avoided, the violation is measuredby the degrees of risks on legal penalty, property damages and personalinjury and thus the negative impact caused by the violation can bemeasured and then minimized.

In the disclosure, a “traffic rule” or one or more “traffic rules” or“information on traffic rules” includes: a legal regulation of a drivingbehavior of a vehicle; a penalty criterion for a violation of trafficrules. The legal regulations pertain to, for example, traffic signals,speed limits, lane attributes (e.g., a passable direction, a busexclusive lane, a passable time period, etc.), lane boundary attributes(e.g., whether a vehicle can cross the lane boundary and change thelane, whether a vehicle can change the vehicle lane and overtake, etc.),etc. The penalty criterion includes, for example, a legal penalty andproperty damage claims for a traffic violation.

In the disclosure, a “traffic rule” or one or more “traffic rules” or“information on traffic rules” includes traffic laws and regulations forhuman drivers, considering that a self-driving vehicle may get involvedin traffic where human-driving vehicles also exist.

In the disclosure, a “traffic rule” or one or more “traffic rules” or“information on traffic rules” may include traffic laws and regulationsfor self-driving vehicles (AV: autonomous vehicles), such as trafficlaws and regulations specified for different levels of autonomousdriving (e.g., semi-autonomous driving, highly autonomous driving andfully autonomous driving).

Considering that different areas (e.g. state, city, town, county,district) or countries may have special traffic laws and regulations oftheir own and that a road section may have temporary trafficregulations, the traffic rule(s) of the disclosure include thosespecified for the area or country in which the vehicle is currentlytravelling and those specified for the road section on which the vehicleis currently travelling.

In the disclosure, a “traffic rule”, or one or more “traffic rules” or“information on traffic rules” may include dynamic updates of thetraffic rules. The dynamic updates, for example, include the updates andchanges on the legal regulation and penalty criterion when the vehiclegoes to a different location (e.g., the vehicle is driving in SanFrancisco one week and then New York the next week, the relevant trafficrules will be updated for the vehicle), and also includes updates on alegal regulation and penalty criterion that change overtime. It alsoincludes any newly enacted laws and regulations pertaining to autonomousdriving. For example, with the developments of artificial intelligenceand law, there may be new laws and regulations taken into force forautonomous driving.

In the disclosure, “autonomous mode” or “autonomous driving” may includedifferent levels of autonomous driving, such as semi-autonomous driving,highly autonomous driving and fully autonomous driving.

FIG. 2 shows a vehicle 100 according to an embodiment of the disclosure.The vehicle 100 is equipped with a driving assistance system and thushas an autonomous driving function. The vehicle 100 can switch between ahuman driver mode and an autonomous mode.

The vehicle 100 is configured with various levels of autonomous drivingcapabilities facilitated by in-vehicle computing systems with logicimplemented in hardware, firmware, and/or software to enable autonomousdriving functions. Such autonomous driving functions may allow vehiclesto self-control or provide driver assistance to detect roadways,navigate from one point to another, detect other vehicles and trafficparticipants (e.g., pedestrians, bicyclists, etc.), detect obstacles andhazards and road conditions (e.g., traffic, road conditions, weatherconditions, etc.), and adjust control and guidance of the vehicleaccordingly. In the disclosure, a “vehicle” may be a manned vehicledesigned to carry one or more human passengers (e.g., cars, trucks,vans, buses, motorcycles, trains, aerial transport vehicles, ambulance,etc.), an unmanned vehicle to drive with or without human passengers(e.g., freight vehicles (e.g., trucks, rail-based vehicles, etc.)),vehicles for transporting non-human passengers (e.g., livestocktransports, etc.). The vehicle may be a special service vehicle forcollecting information of the driving environment, providing assistancefor autonomous driving of other vehicles, performing road maintenancetasks, providing industrial tasks or providing public safety andemergency response tasks, etc.

As shown in FIG. 2, the driving assistance system is provided on/in thevehicle 100, and can also be referred to as an in-vehicle system. Thedriving assistance system includes a communication interface 11, asensing unit 12, and a driving assistance device 13.

The communication interface 11 is configured to wirelessly communicatewith an external device. In other words, the vehicle 100 can exchangeinformation with the external device via the communication interface 11by suitable wireless communication means such as 3G/4G/5G, C-V2X, DSRC,Wi-Fi or Bluetooth. The external device includes a device that is not anintegral part of the vehicle 100. The external device is, for example, acloud server that communicates with the vehicle 100 via a wirelessnetwork, an edge server that communicates with the vehicle 100 via awireless network, a roadside facility that communicates with the vehicle100 via a C-V2X module, another vehicle that is configured to wirelesslycommunicate with the vehicle 100, an electronic device that isconfigured to wirelessly communicate with the vehicle 100 (e.g. asmartphone carried by a pedestrian close by).

The information received from the external device via the communicationinterface 11 and detected by the on-board sensing unit 12 will beprocessed in the driving assistance device 13. The information mayinclude: information on environmental conditions, vehicle states, mapand navigation, and traffic rules.

Information on Environmental Conditions (Environmental Information)

The environmental information may include state information of an objectaround the vehicle 100, particularly state information of an object thatmay collide with the vehicle 100. The state information of the objectmay include the type of the object (e.g. an obstacle, a pedestrian, apet or another vehicle), the state of the object (e.g. being stationaryor moving, moving speed or direction), the position of the object (e.g.an absolute position (such as GNSS, GPS position) or a relative positionto a reference position (such as the distance to a traffic light), andthe size of the object.

The environmental information includes climate and weather information.

The climate and weather information may include features representingweather conditions such as fog, hail, cloudy days, rain, visibility,lighting and rainfall.

The environmental information includes scene information. The sceneinformation may include features representing various scenes such ashighway, intersection and single-lane. The features representing variousscenes may be obtained by semantic recognition (e.g. intelligentsemantic recognition realized by means of AI technology) of theenvironmental information, such as road signs and traffic signs.

Information on Vehicle States (Vehicle State Information)

The vehicle state information may include features representing variousstates of the vehicle such as traveling direction, speed, acceleration,currently travelling lane and motion trajectory.

Information on Map and Navigation

The information on map and navigation includes map data and a navigationroute containing the road section in which the vehicle is travelling.This information can be automatically transmitted to the vehicle fromthe external device based on the location information of the vehicle, ormay be transmitted to the vehicle from the external device in responseto a request from the vehicle to the external device.

The aforementioned information may be collected by the external deviceand processed in the external device, and then the processed informationis sent to the vehicle 100 from a wireless communication unit of theexternal device. This is advantageous as it reduces the amount ofcomputation in the driving assistance device 13 and also reduces therequired computational capacity of the driving assistance device 13.

Information on Traffic Rules (Traffic Rule Information)

The information on traffic rules includes traffic rules for bothhuman-drivers and for autonomous vehicles as well as penalty criteriafor traffic violations. This information may include static trafficrules, dynamic traffic rules and temporary traffic rules.

The “static” traffic rules can be understood as the rules that are notexpected to change over time. The static traffic rules can be obtainedfrom the external device. For example, the static traffic rules caninclude traffic rules that are documented in a traffic rulemanual/guide.

The “dynamic” traffic rules can be understood as the rules that changeover time. For example, the dynamic traffic rules can include newlyenacted traffic rules that are updated and taken into force along withthe developments of artificial intelligence and law (AI Law). Theseupdated traffic rules can be stored in a cloud server and downloaded tothe vehicle.

The “temporary” traffic rules may include the traffic rules instantlydetected by an in-vehicle sensor (such as a dashboard camera) and/or aroad-side sensor that are inconsistent with or missing from the statictraffic rules. For example, a road segment in which the vehicle 100 istravelling is being repaired, a roadblock is set, and a different speedlimit is specified. In this case, the different speed limit belongs to atemporary traffic rule. In addition, the vehicle 100 may send thedetected speed limit to the external device (e.g. uploading to a cloudserver). Then, the speed limit can be sent to vehicles passing by thisroad segment.

It is seen that the vehicle 100 can both receive traffic rules from theexternal device and detect traffic rules temporarily specified for theroad section on which it is travelling. In this way, the traffic rulesto be processed in the driving assistance device 13 fully capture thetraffic rules set for the currently travelling position.

In an example, the traffic rules are pre-processed in the pre-processingmodule of the driving assistance device 13 or in a processor of theexternal device. The pre-processing may include semantic recognition(e.g. natural language semantic recognition) of the traffic rules toretrieve information of interest (i.e., one or more traffic rules) froma vast amount of traffic rules in the database. The pre-processing canfurther include adding tags/labels indicating elements of interest tothe traffic rules. The pre-processing can be implemented using an AImodel (artificial intelligence model) such as a trained neural networkmodel.

Such a pre-processing step is advantageous because useful informationcan be quickly and unambiguously retrieved from a vast amount ofinformation without a time-consuming step of going through all thetraffic rules one by one. Examples of the pre-processing step aredescribed below.

In an example, pre-processing includes adding tags or labelscorresponding to category-based keywords to traffic rules. In thisexample, the pre-processed traffic rules include such tags or labels.The category-based keywords are the keywords classified based on variouscategories. The categories may include a driving behavior category, adriving scene category, a climate and weather category, an object statecategory and a violation category.

Keywords related with the driving scene category may include keywordssuch as “high way”, “tunnel”, and “elevated-highway”. Keywords relatedwith the climate and weather category may include keywords such as“visibility”, “light intensity” and “amount of rain”. Keywords relatedwith the driving behavior category may include keywords such as “vehiclespeed” and “travelling direction”. Keywords belong to the object statecategory may include keywords such as “the object having a likelihood ofcollision with the vehicle”.

In addition, one or more of the categories may have sub-categories. Forexample, the driving scene category includes sub-categories such as highway, one-way street and tunnel. Those sub-categories provide a moredetailed classification of the traffic rules, thereby greatly improvingthe efficiency of retrieving traffic rules.

In another example, pre-processing includes processing traffic rules toinclude different combinations of driving behaviors, driving scenes,weather and climate and object states. For example, each combinationincludes a driving behavior as well as a penalty criterion for thedriving behavior, and also includes one or more of a driving scene,weather and climate and an object state.

In yet another example, pre-processing includes logically processingtraffic rules such that each traffic rule includes two kinds ofelements, namely, a condition element and a result element. Thecondition element may involve a driving behavior and a scene in whichthe driving behavior occurs. The result element may involve a penaltyrule for the driving behavior. The condition element is, for example,“if a driving behavior of a vehicle is triggered (happens) in a certainscenario”. The result element is, for example, “the vehicle should take100% responsibility and pay a fine”. For clarity, an exemplary logicalrule could be described as “if a vehicle is traveling in an autonomousdriving mode in a geofence area and the vehicle speed exceeds 80 km/h,this behavior of the vehicle is illegal and a fine should be paid”.

The sensing unit 12 is provided on/in the vehicle 100, and could bereferred to as an in-vehicle sensing unit. The information detected bythe sensing unit 12 is transmitted to the driving assistance device 13via a vehicle bus.

The sensing unit 12 may include one or more environmental sensors forcapturing environmental information. The one or more environmentalsensors may include one or more camera sensors (e.g. at least one of asingle-target camera, a multi-target camera and a panoramic camera)and/or one or more radar sensors (e.g. at least one of a lidar, anultrasonic radar and a millimeter-wave radar). In an example, two ormore environmental sensors may be arranged at different positions of thevehicle body, and this arrangement ensures safety redundancy, that is,ensures the environmental conditions around the vehicle can beadequately detected.

The sensing unit 12 may further include one or more vehicle statesensors for detecting vehicle state information. The vehicle statesensors may directly or indirectly measure vehicle state parameters. Theone or more vehicle state sensors may include at least one of a wheelspeed sensor, a displacement sensor, an acceleration sensor, and asteering angle sensor.

The driving assistance device 13 is communicatively connected with thecommunication interface 11 and the sensing unit 12, respectively. Atraffic rule database and a plurality of information categories arepre-stored in the driving assistance device 13. The information receivedvia the communication interface 11 and detected by the in-vehicle sensoris classified into respective categories to be further processed in thedriving assistance device 13.

The driving assistance device 13 is configured to acquire informationincluding vehicle state information and/or environment informationsurrounding the vehicle; make a decision on a driving behavior for thevehicle in an autonomous driving mode based on the acquired information;process the acquired information to obtain a respective informationcategory among a plurality of pre-defined information categories theacquired information belongs to; retrieve one or more traffic rulesrelated to the information category the acquired information belongs tofrom a traffic rule database pre-stored in the driving assistancedevice; determine whether the driving behavior violates any of theretrieved traffic rules; and determine risks on legal penalty, propertydamages and personal injury caused by the driving behavior if it isdetermined the driving behavior violates at least one of the retrievedtraffic rules.

In an example, the driving assistance device is further configured toperform a closed-loop control with the determined risks on legalpenalty, property damages and personal injury as feedback parameters andthe closed-loop control is ended when the decision module outputs astable decision. The closed-loop control includes steps (a) and (b)performed sequentially, the steps being repeated until the decisionmodule outputs the stable decision. In step (a), the determining moduleoutputs the determined risks of the driving behavior to the decisionmodule. In step (b), the decision module makes a decision on a newdriving behavior based on the determined risks, and outputs the decisionon the new driving behavior to the determining module.

The driving assistance device 13 may be provided in an ECU, a VCU or adomain controller of the vehicle 100.

The driving assistance device 13 may be implemented by means of hardwareor software or a combination of hardware and software, including anon-transitory computer-readable medium stored in a memory andimplemented as instructions executed by a processor. Regarding the partimplemented by means of hardware, it may be implemented inapplication-specific integrated circuit (ASIC), digital signal processor(DSP), data signal processing device (DSPD), programmable logic device(PLD), field programmable gate array (FPGA), processor, controller,microcontroller, microprocessor, electronic unit, or a combinationthereof. The part implemented by software may include microcode, programcode or code segments. The software may be stored in a machine-readablestorage medium, such as a memory.

In an example, the driving assistance device 13 may include a memory anda processor. The traffic rule database, the plurality of informationcategories and instructions are stored in the memory. The instructions,when executed by the processor, cause the processor to execute thedriving assistance strategy/method of the present disclosure.

FIG. 3 shows a schematic block diagram of a driving assistance device 13according to an embodiment of the disclosure. As shown in FIG. 3, thedriving assistance device 13 includes an acquisition module 131, apre-processing module 132, a decision module 133, and a determiningmodule 134. In an example, the information categories are pre-stored inthe pre-processing module 132 and the traffic rule database ispre-stored in the determining module 134.

It is noted that the driving assistance device 13 and its modules arenamed functionally (logically) and their physical positions are notlimited by their functional names. In other words, the modules may beincluded in the same chip or circuit. The modules may also be providedin different chips or circuits. One or more of these modules may also befurther functionally divided into sub-modules or combined together.

FIG. 4 illustrates a process 400 of determining whether to enable anautonomous driving mode according to an embodiment of the presentdisclosure. The process 400 may be performed in the driving assistancedevice 13, and thus the above described features of the drivingassistance device 13 are applicable here.

As shown in FIG. 4, in block 402, the acquisition module 131 acquiresinformation including traffic rules information as well as environmentinformation and vehicle state information. The acquired information mayinclude information received from the external device and detected bythe in-vehicle sensor.

In block 404, the determining module 134 determines whether the currentstate of the vehicle 100 meets the autonomous driving requirement of thevehicle 100 based on the acquired information, and sends the determinedresult to the decision module 133.

In an example, the determining includes comparing the current state ofthe vehicle 100 with vehicle states that are not suitable for autonomousdriving. If the current state of the vehicle 100 matches at least one ofthe vehicle states that are not suitable for autonomous driving, thedetermining module 134 determines the current state of the vehicle 100does not meet the autonomous driving requirement. If the current stateof the vehicle 100 does not match any of the vehicle states that are notsuitable for autonomous driving, the determining module 134 determinesthe current state of the vehicle 100 meets the autonomous drivingrequirement. The vehicle states that are not suitable for autonomousdriving may include: the vehicle door or the trunk cover being in anopen state; the orientation of the vehicle head not being conformingwith the driving direction guided by a navigation route; a failure of anin-vehicle sensor for autonomous driving being detected; a failure ofthe positioning function or navigation function being detected; and thecurrent travelling area is not a legally allowed autonomous drivingarea.

In block 406, the decision module 133 makes a decision on whether toenable the autonomous driving of the vehicle 100 based on the determinedresult.

If the determined result is that the current state of the vehicle 100meets the autonomous driving requirement, the decision module 133 makesa decision to enable the autonomous driving of the vehicle 100. Then,the driving assistance device 13 implements a driving assistance process500 that will be described below.

If the determined result is that the current state of the vehicle 100does not meet the autonomous driving requirement, the decision module133 makes a decision to disable the autonomous driving of the vehicle100. In this case, the driving assistance device 13 will provide areminder to the driver of the vehicle 100 to adjust the state of thevehicle to meet the autonomous driving requirement. Then, the decisionto enable the autonomous driving can be made.

FIG. 5A shows an exemplary working principle of the driving assistancedevice 13. FIG. 5B is a flowchart illustrating a driving assistanceprocess 500 according to an example of the disclosure. The drivingassistance process 500 may be performed in driving assistance device 13,and thus the above described features of the driving assistance device13 are also applicable here. The driving assistance process 500 will bedescribed with the reference to FIGS. 5A and 5B.

In block 502, similar to block 402, the acquisition module 131 acquiresinformation including traffic rules information as well as environmentinformation and vehicle state information. The acquired information mayinclude information received from the external device and informationdetected by the in-vehicle sensor.

In block 504, the decision module 133 makes a decision on a drivingbehavior of the vehicle 100 based on its input. The decision on adriving behavior is the output of the decision module 133. The input ofthe decision module 133 includes the acquired information from theacquisition module 131 and the determination of the violation of thedriving behavior from the determining module 134. The driving behaviormay include various driving behavior of the vehicle 100 in theautonomous mode, such as left turning, accelerating, braking, etc.

In block 506, the pre-processing module 132 pre-processes the acquiredinformation. The pre-processing may include processing the acquiredinformation into certain information categories using a classificationalgorithm (block 5061) and performing a fusion calculation on thecategorized acquired information (block 5062).

The pre-processing module 132 stores various predefined informationcategories. These information categories correlates, at least in part,to the labels/tags of the traffic rules (which corresponds to thecategory-based keywords as discussed above). The information categoriesare considered and referred to when generating the labels/tags. In thisway, the efficiency of pre-processing can be improved because theprocessed information is interrelated. Examples of blocks 5061 and 5062will be described.

In block 5061, the acquired information is processed using aclassification algorithm to obtain one of the plurality of informationcategories.

Referring to FIG. 5A, in an example, the pre-defined informationcategories are stored in the pre-processing module 132, including adriving scene category, a weather condition and environment category, avehicle state category and an object state category. One or more of theinformation categories may each include sub-categories. It is noted thatthe information categories and sub-categories shown in FIG. 5A areexemplary. According to examples of the disclosure, the informationcategories may include fewer or more categories, and the sub-categoriesmay be classified in other ways.

In an example, the same type of information that is captured bydifferent sensors (e.g. the communication interface and the in-vehiclesensor) is classified into the same respective one of the informationcategories. For example, if the acquired information includes theillumination intensity received via the communication interface and theillumination intensity sensed by the in-vehicle sensor, both of theintensities will be classified into the category of weather conditionsand environments. In addition, if this category has a sub-category ofillumination, both of the intensities will be classified into thesub-category of illumination.

In block 5062, a fusion calculation is performed on information acquiredby different sensors that is classified into the same informationcategory. For example, the fusion calculation is performed on both ofthe illumination intensities to obtain the fused intensity. Then, thefused intensity will be used as the information on illumination and befurther processed in the driving assistance device 13.

It is advantageous to provide the plurality of categories of informationin the driving assistance device 13 because the acquired information isclassified into respective categories, and the subsequent informationprocessing is performed with the classified information. In this way, areduced set of information will be processed due to the redundancy inthe acquired information being removed. With reduced set of information,the complexity of the subsequent information processing can be greatlyreduced and the efficiency can be improved. For example, the determiningis performed by the determining module 134 using the pre-processedinformation output from the pre-processing module 132, and thus theefficiency of the determining module 134 can be improved.

In block 508, the determining module 134 retrieves one or more trafficrules from the traffic rule database based on the pre-processedinformation.

In an example, the determining module 134 matches the pre-processedinformation with the pre-processed traffic rules to retrieve the one ormore traffic rules. For example, the determining module 134 matches theinformation category and/or sub-category of the pre-processedinformation with the labels/tags of the processed traffic rules, andretrieves a traffic rule with a tag that matches the informationcategory or sub-category of the pre-processed information. Taking theillumination intensity as an example, if the pre-processed informationis classified into the sub-category of illumination, and thesub-category of illumination matches with the label of illumination,then one or more traffic rules having the label of illumination will beretrieved from the traffic rule database.

In another example, the determining module 134 retrieves the one or moretraffic rules by means of a traffic rule model. The traffic rule modelmay be a trained neural network model. The pre-processed information(e.g., the classified information and the fused information) is input tothe traffic rule model, and the traffic rule model outputs one or moretraffic rules related to the input information.

It is seen that, upon pre-processing the acquired information and thetraffic rules, the related one or more traffic rules can be retrievedquickly and accurately without traversing all the traffic rules in thetraffic rule database, which greatly improves the operating efficiencyof the determining module 134.

In block 510, the determining module 134 evaluates the driving behaviordecided by the decision module 133 from three aspects, namely, the legalpenalty risk, the property damages risk and the personal injury risk.The determining module 134 first determines whether the driving behaviorviolates at least one of the retrieved traffic rules. If the determiningmodule 134 determines the driving behavior does not violate any of theretrieved traffic rules, a message that the driving behavior does notviolate any of the traffic rules will be provided. If the determiningmodule 134 determines the driving behavior violates at least one of theretrieved traffic rules, the determining module 134 judges the drivingbehavior from the three aspects as further described below.

For the first aspect, the determining module 134 determines the legalpenalty risk of the driving behavior based on the retrieved trafficrules. The legal penalty risk may be expressed in terms of the degree oflegal responsibility corresponding to at least one of the retrievedtraffic rules that the driving behavior violates.

In an example, the degree of legal responsibility is expressed by apercentage of fault between 0% and 100%. The percentage of 100% means afull responsibility and complete fault. The percentage of 0% means noresponsibility and no fault. The percentage of 50% means halfresponsibility and half fault.

In another example, the degree of legal responsibility is expressed byseveral levels (e.g., levels 1˜n, n being a natural number greater than1). The level varies as the legal responsibility of the vehicle 100 forthe driving behavior changes. For clarity, an example of levels 1-7 isdescribed. In this example, In an example, levels 1-7 are preset, level1 represents a full responsibility; level 2 represents a 80%-100%responsibility; level 3 represents a 60%-80% responsibility; level 4represents a 40%-60% responsibility; level 5 represents a 20%-40%responsibility; level 6 a 0%-20% responsibility; and level 7 representsno responsibility.

For the second aspect, the determining module 134 determines theproperty damages risk of the driving behavior based on the retrievedtraffic rules. The property damages risk may be expressed in terms ofthe amount of the fine and payment corresponding to the violation of atleast one of the retrieved traffic rules that caused by the drivingbehavior.

In an example, the property damages risk may be expressed by severallevels (e.g., levels 1˜m, m being a natural number greater than 1). Forexample, level 1 represents the maximum amount of the fine and payment,and level m represents the minimum amount of the fine and payment. Thelarger the number of the level is, the less amount of the fine andpayment is needed.

It is noted that the range of the fine and payment may be referred tothe range of the fine and payment regulated by a special traffic rule orthe range of the fine pre-stored in the traffic rule database and whichmay also relate to the type of object the autonomous vehicle may becollide with (e.g. a premium luxury brand passenger vehicle or a low-endpassenger vehicle; a curb or a large billboard).

For the third aspect, the decision module 134 determines (predicts) thepersonal injury risk of the driving behavior based on the retrievedtraffic rules. The personal injury risk may be expressed in terms of thedegree of personal injury caused by the driving behavior of the vehicle100. The degree of personal injury may be calculated based on predictedinjuries and possible casualties. For example, if the vehicle 100 willcollide with a pedestrian with a speed of 40 km/h under the decidedvehicle behavior, personal injury risk will be calculated by means ofpredicting the extent of the personal injuries of the pedestrian in thiscase.

In an example, the personal injury risk may be expressed by severallevels of severity (e.g., levels 1-t, t being a natural number greaterthan 1). The level varies as the severity degree of personal injury thatis caused by driving behavior changes. For example, level 1 representsthe most severe personal injury and level t represents n the leastsevere personal injury.

A few examples of determinations made by the determining module 134 indifferent scenarios are further illustrated in Table 1 below. As shownthe degree of personal injury is described by levels 1-5 with level 1representing the most serious injury and level 5 representing no injury.The degree of property damages is described by levels 1-5 with level 1representing the upper limit of the fine and payment and level 5representing the lower limit of the fine and payment. The legalityrefers to whether the driving behavior of the vehicle 100 complies withthe retrieved traffic rules, wherein “Yes” indicates the drivingbehavior of the vehicle 100 complies with the retrieved traffic rules,and “No” indicates the driving behavior of the vehicle 100 does notcomply with at least one of the retrieved traffic rules.

TABLE 1 Examples of the determinations Degree Degree Decided Degree ofof of Pre-processed driving legal personal property information behaviorLegality responsibility injury damages E_1 Scene: highway Emergency NoThe host Level 1 Level 1 Weather and braking vehicle: 30% climate:Collision The visibility <300 m with the pedestrian: Vehicle state:pedestrian 70% 100 km/h, below the speed limit Object State: apedestrian in front is crossing the driving lane of the vehicle E_2Scene: highway Emergency No The host Level 1 Level 1 Weather and brakingvehicle: climate: Change to 100% visibility <300 m the reverse Thetrunk: Vehicle state: lane 0% 100 km/h, below the Collision speed limitwith the Object state: a truck truck on a reverse lane, 80 km/h, belowthe speed limit E_3 Scene: highway Emergency No The host Level 3 Level 3Weather and braking vehicle: climate: Turn to the 100% visibility <300 mright The object: Vehicle state: Crash into 0% 100 km/h, below the thewall speed limit with a Object state: wall on collision the right angleof 45 degrees E_4 Scene: urban Crossing No The host Level 5 Level 3 roadthe solid vehicle: Weather and line to the 100% climate: visibility buslane on The object: <500 m the right 0% Vehicle state: below Nocollision the speed limit Object State: a pedestrian in front iscrossing the driving lane of the vehicle E_5 Scene: urban Cross a No Thehost Level 5 Level 2 road solid vehicle: Weather and double line 100%climate: visibility to a reverse The object: <500 m lane on the 0%Vehicle state: below left the speed limit No collision Object State: apedestrian in front is crossing the driving lane of the vehicle

After determining the legal penalty risk, the property damages risk andthe personal injury risk, the determining module 134 outputs thedetermined results to the decision module 133.

Then, a closed-loop control is performed by the decision module 133 andthe determining module 134. The determined risks are used as feedbackparameters in the closed-loop control, and the closed-loop control iscompleted when the decision on the driving behavior does not change,that is, the output of the decision module 133 is stable. In asimplified example, the closed-loop control includes cyclicallyperforming the following steps (a) and (b) until the output of thedecision module does not change as further explained below.

In step (a), upon a first (or a preset) driving behavior being inputinto the determining module 134, the determining module 134 outputs thedetermined risks of said first driving behavior to the decision module133. In step (b), the decision module 133 decides a new driving behaviorattempting to reduce the risks, and outputs the second driving behaviorto the determining module 134 such that the determining module 134determines risks on legal penalty, property damages and personal injuryof the second driving behavior. If the determined risks of the seconddriving behavior are lower than the first driving behavior, said lowerrisks will be fed to the decision module 133 and trigger the decisionmodule 133 to decide the third driving behavior attempting to furtherreduce the risks of the second decision. If the determined risks of thesecond driving behavior are higher than the first driving behavior, thedecision module 133 will decide a third behavior and output said thethird behavior to the determining module 134. The determining module 134will determine the risks of said third behavior and compare the risks ofsaid third behavior with the risk of the first behavior. If the risks ofthe third behavior are lower than the first behavior, the determiningloop will decide a fourth behavior attempting to further reduce therisks of the third behavior. If the risks of the third behavior arehigher than the first behavior, the determining loop will decide afourth behavior attempting to further reduce the risks of the firstbehavior. The loop will continue until the decided risks of the newdriving behavior could not be further lowered as compared to the lowestrisks so far.

For example, if the calculated risks in n consecutive cycles (n can bepredefined, or determined by the decision module in each specific case)are all higher than the lowest risks so far (the baseline), the decisionmodule reverts to the driving behavior with the lowest risks. At thattime, the decision module 133 will revert to the previous decision ofthe driving behavior with the lowest risks. The loop will end and thedecision module 133 outputs a stable decision.

In an example, the determining module 134 calculates a consolidated riskscore for a decided driving behavior based on the determined risks. Theconsolidated risk score is a quantization parameter indicating aconsolidated risk of a decided driving behavior. In an embodiment, afterdetermining risks on different aspects including the abovementionedthree aspects (legal penalty, property damages and personal injurycaused by a driving behavior) and converting all these determined risksinto values in the same variable scale (such as numbers within 0-10 orlevels in 1-7), the determining module 134 calculates a weighted averageof the determined risks on these aspects as the consolidated risk score.This is particularly useful when some aspects are considered moreimportant than other aspect in determining the overall and consolidatedrisk of a decided driving behavior. For example, the personal injuryrisk would be considered more important than the legal penalty and theproperty damages in assessing the overall risks of a driving behavior,the personal injury risk will be assigned more weight than the legalpenalty and the property damages, and contribute more to the finalaverage.

In other words, the closed-loop control includes circularly carrying outblocks 504 and 510 with the determined risks or the consolidated riskscore as the feedback parameter as illustrated above. It is noted thatthe inputs to the decision module include the determinations from thedetermining module as well as the information from the acquisitionmodule.

An example of the closed-loop control will be described using examples1-3 in Table 1.

Referring to Table 1, the scene and the weather in each of examples 1-3are the same. In a first cycle (example 1), the decision module makes afirst decision of emergency braking, which will result in collision withthe pedestrian. The first decision is output to the determining module134. The determining module 134 determines the legality (illegal), thedegree of legal responsibility (30% fault), the degree of personalinjury (level 1) and the degree of property damages (level 1) for thefirst decision. The determined results are output to the decision module133. In a second cycle of example 2, the decision module 133 makes asecond decision attempting to reduce the risks for emergency braking andchanging to the reverse lane, which will result in collision with thetruck. The second decision is output to the determining module 134. Thedetermining module 134 determines the legality (illegal), the degree oflegal responsibility (100%), the degree of personal injury (level 1) andthe degree of property damages (level 1) for the second decision. Thedetermined risks which are higher than the risks of cycle 1, are outputto the decision module 133. In a third cycle of example 3, the decisionmodule 133 makes a third decision attempting to reduce the risks usingthe risks of cycle 1 as the baseline (as risks of cycle 2 are higherthan cycle 1) for emergency braking and turning to the right, which willresult in crashing into the wall with a collision angle of 45 degrees.The third decision is output to the determining module 134. Thedetermining module determines the legality (illegal), the degree oflegal responsibility (100%), the degree of personal injury (level 3) andthe degree of property damages (level 3) for the third decision. Thedetermined risks are lower than the risks of cycle 1 and are output tothe decision module 133. In this specific scene, the decision module 133decides there is no other different driving behavior options available,the decision module makes a fourth decision, which is the same as thethird decision and thus outputs a stable decision and ends the loop.Assuming there are additional driving decision options are available,the decision module 133 will makes a number of new decisions (not shown)and the determining module 134 will calculate the risks thereof (notshown). When the risks of these new decisions are all higher than therisks of cycle 3, the decision module 133 then reverts to the thirddecision and outputs the third decision as the stable decision. Theclosed-loop will end then. It is seen that the closed-loop control isadvantageous because both the personal injury and the property damagesare reduced through the cycles. To be more specific, both the degree ofpersonal injury and the degree of property damages are decreased toLevel 3 (a better level) from Level 1 (a worst level).

The determining of whether a stable decision is output is performed indecision block 133 (block 512). In an example, in the case that thedriving scene stays the same, the determining module determines whetherthe risks of the present driving decision are higher than the lowestrisks so far (the baseline). If the calculated risks in n consecutivecycles (n can be predefined, or determined by the decision module ineach specific case) are all higher than the baseline, the decisionmodule reverts to the driving behavior with the lowest risks, determinesthat a stable decision can now be output, and outputs the stabledecision as the final decision. The loop can then be completed.

In another embodiment of the closed-loop control, the determining module134 is set to compare the determined risks of the current cycle with thedetermined risks of the last cycle, rather than the cycle that has thelowest determined risks so far. For example, the determining module 134compares the determined risks of the fourth cycle with that of the thirdcycle even though the risks of the second cycle are lower than the thirdcycle. In this example, blocks 504 and 510 are circularly carried outfor a pre-determined number of consecutive cycles until the output ofthe decision module 133 does not change. Then, the decision module 133outputs the stable decision as the final decision.

It is noted that, in the case that a stable decision is output, aclosed-loop system including the decision module and the decision moduleis in a stable state.

It is noted that, in one closed-loop, elements in the scene (e.g. thevehicle state and the object state) may dynamically change; however, thescene can be understood as being unchanged in terms of the trafficrules. In other words, a slight change in the scene will not change thescene defined by controlling traffic rules. For example, referring toFIG. 1A, the following scenario can be seen as being the same scene: thepedestrian moves with a speed of 1 meter/second without changing thelane. Referring to FIG. 1B, the following scenarios can be seen as thesame scene: the bicycle or the bus traveling in a neighboring laneslightly moves; however this movement cannot provide sufficient spacefor the vehicle V to avoid the collision and the vehicle V still facethe above three options.

It is noted that the stable decision means the decided driving behaviordoes not change; however, the decided driving behavior can be achievedby different vehicle maneuvers. For example, in a closed-loop of aboutone millisecond, the stable (final) decision is to change to the leftlane. For achieving this final decision, the vehicle may turn to theleft with 10 degrees or turn to the left with 11 degrees.

In an example of the disclosure, the time period during which thedecision module and the determining module perform one closed-loopcontrol is in the order of milliseconds. In such a short time period,the elements in the scene are considered unchanged. For example, thevehicle state and the object state do not change. In other words, theclosed-loop system including the decision module and the determiningmodule becomes stable before the scene changes.

According to examples of the disclosure, the decision on the drivingbehavior is made based on the aspects that can be measured, i.e., riskson legal penalty, property damages and personal injury of the drivingbehavior. In this way, the risk of the driving behavior can be decreasedto the minimum level.

According to examples of the disclosure, the closed-loop control isperformed so as to generate an optimal decision for the vehicleefficiently.

According to examples of the disclosure, the closed-loop control isperformed using the pre-processed information that is interrelated. Inthis way, the closed-loop control can be performed with efficiency.

FIG. 6 illustrates a driving assistance method 600 according to anembodiment of the disclosure. The method 600 may be executed in thedriving assistance device 13 or the driving assistance system 10.Therefore, the above descriptions about the driving assistance device 13and the driving assistance system 10 are also applicable here.

Referring to FIG. 6, in step 602, vehicle state information andenvironment information are acquired.

In step 604, a decision on a driving behavior is made for the vehicle inan autonomous driving mode based on the acquired information.

In step 606, the acquired information is processed to identify one of aplurality of pre-defined information categories the acquired informationbelongs to.

In step 608, one or more traffic rules related to the informationcategory the acquired information belongs to are retrieved from atraffic rule database pre-stored in the driving assistance device.

In step 610, a determination of whether the driving behavior violatesany of the retrieved traffic rules is made.

If it is determined the driving behavior violates at least one of theretrieved traffic rules, the method proceeds to step 612. In step 612,risks on legal penalty, property damages and personal injury of thedriving behavior are determined as described above and a close loop isrun to determine the optimized driving decision based on the determinedrisks as described above.

If it is determined the driving behavior does not violate any of theretrieved traffic rules, the method proceeds to step 614. In step 614, amessage of “no violation” is output.

The disclosure provides a non-transitory computer-readable medium withinstructions stored therein which, when executed, causes a processor tocarry out the steps of the driving assistance method 600 describedabove.

It is noted that all the operations in the method described above aremerely exemplary, and the disclosure is not limited to any operations inthe method or sequence orders of these operations, and should cover allother equivalents under the same or similar concepts.

The processors can be implemented using electronic hardware, computersoftware, or any combination thereof. Whether these processors areimplemented as hardware or software will depend on the specificapplication and the overall design constraints imposed on the system. Byway of example, a processor, any portion of a processor, or anycombination of processors presented in this disclosure may beimplemented as a microprocessor, a micro-controller, a digital signalprocessor (DSP), a field programmable gate array (FPGA), a programmablelogic device (PLD), state machine, gate logic, discrete hardwarecircuitry, and other suitable processing components configured toperform the various functions described in this disclosure. Thefunctions of a processor, any portion of a processor, or any combinationof processors presented in this disclosure may be implemented assoftware executed by a microprocessor, a micro-controller, a DSP, orother suitable platforms.

Software should be interpreted broadly to represent instructions,instruction sets, code, code segments, program code, programs,subroutines, software modules, applications, software applications,software packages, routines, subroutines, objects, running threads,processes, functions, and the like. Software can reside on anon-transitory computer-readable medium. Such non-transitorycomputer-readable medium may include, for example, a memory, which maybe, for example, a magnetic storage device (e.g., a hard disk, a floppydisk, a magnetic strip), an optical disk, a smart card, a flash memorydevice, a random access memory (RAM), a read only memory (ROM), aprogrammable ROM (PROM), an erasable PROM (EPROM), an electricallyerasable PROM (EEPROM), a register, or a removable disk. Although amemory is shown as being separate from the processor in various aspectspresented in this disclosure, a memory may also be internal to theprocessor (e.g., a cache or a register).

The previous description is provided to enable any person skilled in theart to practice the various aspects described herein. Variousmodifications to these aspects will be readily apparent to those skilledin the art, and the generic principles defined herein may be applied toother aspects. Thus, the claims are not intended to be limited to theaspects shown herein. All structural and functional equivalenttransformations to the elements of the various aspects of thedisclosure, which are known or to be apparent to those skilled in theart, are intended to be covered by the claims.

1. A driving assistance device for a vehicle, comprising: an acquisitionmodule configured to acquire information comprising at least one ofvehicle state information and environment information surrounding thevehicle; a decision module configured to determine a driving behaviorfor the vehicle in an autonomous driving mode based on the acquiredinformation; a pre-processing module configured to process the acquiredinformation to identify a respective information category among aplurality of pre-defined information categories the acquired informationbelongs to; and a determining module configured to retrieve one or moretraffic rules related to the information category the acquiredinformation belongs to from a traffic rule database pre-stored in thedriving assistance device; determine whether the driving behaviorviolates any of the retrieved traffic rules; and determine risks of saiddriving behavior if it is determined the driving behavior violates atleast one of the retrieved traffic rules, said risks comprising one ormore of the legal penalty, property damages and personal injury causedby the driving behavior.
 2. The driving assistance device according toclaim 1, wherein the decision module and the determining module areconfigured to perform a closed-loop control with the determined risks asfeedback parameters, and the closed-loop control is ended when saiddetermined risks of the driving behavior could not be further lowered.3. The driving assistance device according to claim 2, wherein theclosed-loop control includes steps (a) and (b) performed sequentially,the steps being repeated until the decision module outputs the stabledecision: in step (a), the determining module outputs the determinedrisks of the driving behavior to the decision module; and in step (b),the decision module determines a new driving behavior attempting toreduce the determined risks, and outputs the decision on the new drivingbehavior to the determining module.
 4. The driving assistance deviceaccording to claim 1, wherein traffic rules in the traffic rule databaseare pre-processed to include at least one of: labels indicative of oneor more of vehicle states, scenes, weather, object states, and penaltycriterion; combinations of driving behaviors with surroundings of thevehicle including driving scenes, weather and climate and object states;and a pair of condition element and result element, the conditionelement comprising at least one driving behavior and a scene in whichthe driving behavior occurs, and the result element comprising a penaltycriterion associated with the driving behavior.
 5. The drivingassistance device according to claim 4, wherein the labels at leastpartially correspond to the pre-defined information categories, andwherein the determining module is configured to retrieve the one or moretraffic rules with the labels that matches the information category theacquired information belongs to.
 6. The driving assistance deviceaccording to claim 4, wherein the combinations at least partiallycorrespond to the pre-defined information categories, and wherein thedetermining module is configured to retrieve the one or more trafficrules with the combinations that matches the information category theacquired information belongs to.
 7. The driving assistance deviceaccording to claim 1, wherein the determining module is configured toretrieve the one or more traffic rules using a traffic rule model; andwherein the pre-processed information is an input to the traffic rulemodel, and the one or more traffic rules are output from the trafficrule model.
 8. The driving assistance device according to claim 1,wherein the pre-defined information categories include at least one of adriving scene category, a weather and environment category, a vehiclestate category and an object state category, and wherein one or more ofthe pre-defined information categories comprise sub-categories.
 9. Thedriving assistance device according to claim 1, wherein the traffic ruledatabase comprises one or more of: one or more static traffic rulesassociated with the vehicle's location; one or more dynamic trafficrules that are updated over time; and one or more temporary trafficrules detected by at least one of an on-board sensor and a road-sidesensor, said temporary traffic rules are inconsistent with or absentfrom the static traffic rules.
 10. The driving assistance deviceaccording to claim 1, wherein the pre-processing module is configuredto: identify acquired information received from different sensors thatbelong to the same information category; and perform a fusioncalculation of the identified information in said same informationcategory and output the fused information to the determining module, thedetermined risks being based on the fused information.
 11. The drivingassistance device according to claim 1, wherein said legal penaltycomprising a degree of legal responsibility for violating at least oneof the retrieved traffic rules, said degree of legal responsibility isexpressed as a percentage between 0% and 100% of fault or one ofseverity levels of fault or continuous values in an adjustable range.12. The driving assistance device according to claim 1, wherein saidproperty damages for violating the at least one of the retrieved trafficrules are expressed as one of severity levels based on the amount of theproperty damages caused by the violation.
 13. The driving assistancedevice according to claim 1, wherein said personal injury is expressedas one of severity levels based on predicted injuries and relatedcasualties caused by the violation.
 14. The driving assistance deviceaccording to claim 2, wherein the decision module is further configuredto: enable the autonomous driving mode based on the acquired informationif the vehicle meets autonomous driving requirements, and enable theclosed-loop control; and disable the autonomous driving mode if thevehicle does not meet the autonomous driving requirements, and disablethe closed-loop control.
 15. A vehicle, comprising: a communicationinterface configured to receive information on the vehicle's states,environmental conditions and traffic rules from an external device; anon-board sensing unit for capture information on the vehicle'ssurroundings and states; and a driving assistance device incommunication with the communication interface and the in-vehiclesensing unit, the driving assistance device comprising: an acquisitionmodule configured to acquire information from the communicationinterface and the in-vehicle sensing unit; a decision module configuredto determine a driving behavior for the vehicle in an autonomous drivingmode based on the acquired information; a pre-processing moduleconfigured to process the acquired information to identify a respectiveinformation category among a plurality of pre-defined informationcategories the acquired information belongs to; and a determining moduleconfigured to retrieve one or more traffic rules related to theinformation category the acquired information belongs to from a trafficrule database pre-stored in the driving assistance device; determinewhether the driving behavior violates any of the retrieved trafficrules; and determine risks of said driving behavior if it is determinedthe driving behavior violates at least one of the retrieved trafficrules, said risks comprising one or more of the legal penalty, propertydamages and personal injury caused by the driving behavior.
 16. Adriving assistance method for a vehicle, comprising the steps of:acquiring information including at least one of vehicle stateinformation and environment information surrounding the vehicle;determining a driving behavior for the vehicle in an autonomous drivingmode based on the acquired information; processing the acquiredinformation to obtain a respective information category among aplurality of pre-defined information categories the acquired informationbelongs to; retrieving one or more traffic rules related to theinformation category the acquired information belongs to from a trafficrule database pre-stored in the driving assistance device; determiningwhether the driving behavior violates any of the retrieved trafficrules; and determining risks of the driving behavior if it is determinedthe driving behavior violates at least one of the retrieved trafficrules, said risks comprising one or more of the legal penalty, propertydamages and personal injury caused by the driving behavior. 17.Anon-transitory computer-readable medium with instructions storedtherein which, when executed, cause one or more processors to carry outthe steps comprising: acquiring information including at least one ofvehicle state information and environment information surrounding thevehicle; determining a driving behavior for the vehicle in an autonomousdriving mode based on the acquired information; processing the acquiredinformation to obtain a respective information category among aplurality of pre-defined information categories the acquired informationbelongs to; retrieving one or more traffic rules related to theinformation category the acquired information belongs to from a trafficrule database pre-stored in the driving assistance device; determiningwhether the driving behavior violates any of the retrieved trafficrules; and determining risks of the driving behavior if it is determinedthe driving behavior violates at least one of the retrieved trafficrules, said risks comprising one or more of the legal penalty, propertydamages and personal injury caused by the driving behavior.